{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x=[1,2,3,4,5,6,7]\n",
    "y=[50,51,52,48,47,49,46]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16ff68db8d0>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xv92gegP+2uGv9GndJ8BUiatp7aZMu2oal7e+PL+tVf1WPNn1yUDXEFiwZgGX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\nPkqA40EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16ff694f470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Day')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Weather')\n",
    "plt.plot(x,y,color='green',linewidth=5, linestyle='dotted')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
